Root exudates are plant metabolites secreted from the roots into the soil. These exudates are involved in many important biological processes, including acquisition of nutrients, defense and signaling to rhizosphere bacteria, such as isoflavones of soybean crucial for the symbiosis with rhizobium. Less is known, however, about other types of root exudates. This study shows that soybean roots secrete large amounts of soyasaponins (triterpenoid glycosides) as root exudates. The soyasaponins are classified into four groups, with group A being the most secreted of these compounds, whereas DDMP (2,3-dihydro-2,5-dihydroxy-6-methyl-4H-pyran-4-one) soyasaponins is the group showing greatest accumulation in root tissues, suggesting a selection system for secreted compounds. Time-course experiments showed that the soyasaponin secretion peaked during early vegetative stages. In particular, soyasaponin Ah was the major compound secreted by soybean roots, whereas the deacetylated derivative Af was the major compound secreted specifically during the VE stage. The secretion of soyasaponins containing glycosyl moieties is an apparent loss of photosynthates. This phenomenon has been also observed in other legume species, although the composition of secreted soyasaponins is plant species dependent. The identification of triterpenoid saponins as major metabolites in legume root exudates will provide novel insights into chemical signaling in the rhizosphere between plants and other organisms.

The term "rhizosphere" was coined by L. Hiltner in 1904 and refers to "the zone of soil surrounding the root which is affected by it" (Hartmann, Rothballer, & Schmid, 2008, Hiltner, 1904). Plant roots function as an anchor that supports the plant body and absorb nutrients and water; they also secrete a variety of plant-derived metabolites into the rhizosphere, which include low-molecular weight compounds, such as amino acids, sugars, phenolics, terpenoids, and lipids, and high-molecular weight compounds, such as proteins, polysaccharides, and nucleic acids, depending on the growth stage and environmental conditions (Massalha, Korenblum, Tholl, & Aharoni, 2017). The amount of these root exudates is large (up to 40% of all carbon fixed by photosynthesis can be released from plant roots

The hedgehog (Hh) signaling pathway has crucial roles in embryonic development, cell maintenance and proliferation, and is also known to contribute to cancer cell growth. New naturally occurring Hh inhibitors (1, 7 and 9) were isolated from Vitex negundo using our previously constructed cell-based assay. Bioactivity guided isolation provided 9 natural compounds including a new diterpene, nishindanol (9). Compounds 7 and 9 showed cytotoxicity against cancer cell lines in which Hh signaling was aberrantly activated. Vitetrifolin D (7; GLI1 transcriptional inhibition IC50 = 20.2 μM) showed inhibition of Hh related protein (PTCH and BCL2) production. Interestingly, the constructed electrophoresis mobility shift assay revealed that vitetrifolin D (7) disrupted GLI1 binding on its DNA binding domain. epi-Sclareol (8; inactive), possessing a similar structure to 7, did not show inhibition of GLI1–DNA complex formation. This is the first example of naturally occurring inhibitors of GLI1–DNA complex formation.

The first study on chemical constituents and biological activities of Clausena lansium (Lour.) Skeels (Rutaceae) growing in Vietnam has been done. Phytochemical investigation of n-hexane extract led to the isolation of five compounds: dihydroindicolactone (1), 8-geranyloxy psoralen (2), imperatorin (3), heraclenol (4) and indicolactone (5), in which this is the first report on the presence of dihydroindicolactone (1). Their structures were elucidated based on LC/MS/NMR hyphenated techniques as well as comparison with those of literature data. The n-hexane extract and its subfractions, ethanol 95% extract and several isolated compounds were evaluated for antifungal activity.

The hedgehog (Hh) signaling pathway performs important roles in embryonic development and cellular proliferation and differentiation. However, in many cancer cells Hh signaling is aberrantly activated, which has provided a strong impetus for the development of Hh pathway inhibitors. To address this, we synthesized a series of heterocyclic flavonoids and evaluated their Hh signaling inhibitory activity on cancer cell lines using our cell-based assay system. Of the synthetic flavonoids, compounds 4a and g showed good inhibitory activity (IC 50 was 16.8 and 21.8 µM, respectively), and were cytotoxic toward human pancreatic (PANC1) and prostate (DU145) cancer cells in which Hh signaling was activated. Compounds 4a and g had moderate selectivity against PANC1 cells. Western blotting analyses revealed that PTCH and GLI1 expression was reduced after treatment with these compounds. Overall, these synthetic flavonoids represent promising new additions to our expanding panel of Hh pathway inhibitors, and with further development these molecules may ultimately be considered for clinical use.

BACKGROUND Human health status can be measured in several different ways and statistical relationships among various measurements can be represented as a joint probability distribution. Approximation of the current health status of individuals will allow for more personalized and preventive healthcare by informing the potential risks and developing personalized interventions. Understanding the modifiable risk factors related to lifestyle, diet, and physical activity will facilitate the design of optimal treatment approaches for individuals. OBJECTIVE This study aims to provide a high-dimensional, cross-sectional dataset of comprehensive healthcare information to construct a virtual human generative model (VHGM) based on a joint probability distribution. METHODS In this cross-sectional observational study, data will be collected from a population of 1000 adult men and women (aged ≥20 years) matching the age ratio of the typical adult Japanese population. Data will include biochemical and metabolic profiles from blood, urine, saliva, and oral glucose tolerance tests; bacterial profiles from feces, facial skin, scalp skin, and saliva; mRNA, proteome, and metabolite analyses from facial and scalp skin surface lipids; lifestyle survey and questionnaire; physical, motor, cognitive, and vascular function analyses; alopecia; and comprehensive analyses of body odor components. Statistical analyses will examine multiple health-related items using a joint probability distribution model. We will train a joint probability distribution, the VHGM, by combining a commercially available healthcare dataset containing large amounts of relatively low-dimensional data with a high-dimensional, cross-sectional dataset. The trained VHGM is expected to enable various healthcare applications through application program interface calls. RESULTS Written informed consent will be required to participate in the study. The study has been approved by the Institutional Review Boards of the Kao Corporation (Approval # K0023-2108) and the Preferred Network, Inc. (Approval # ET22110047). CONCLUSIONS The collected data are expected to provide information on the relationships between various health statuses. Because different degrees of health status correlations are expected to have different effects on individual health status, this study will contribute to developing empirically justified interventions based on the population. CLINICALTRIAL The trial is registered with the University Hospital Medical Information Network (Registration No. UMIN000045746).

Background Human health status can be measured on the basis of many different parameters. Statistical relationships among these different health parameters will enable several possible health care applications and an approximation of the current health status of individuals, which will allow for more personalized and preventive health care by informing the potential risks and developing personalized interventions. Furthermore, a better understanding of the modifiable risk factors related to lifestyle, diet, and physical activity will facilitate the design of optimal treatment approaches for individuals. Objective This study aims to provide a high-dimensional, cross-sectional data set of comprehensive health care information to construct a combined statistical model as a single joint probability distribution and enable further studies on individual relationships among the multidimensional data obtained. Methods In this cross-sectional observational study, data were collected from a population of 1000 adult men and women (aged ≥20 years) matching the age ratio of the typical adult Japanese population. Data include biochemical and metabolic profiles from blood, urine, saliva, and oral glucose tolerance tests; bacterial profiles from feces, facial skin, scalp skin, and saliva; messenger RNA, proteome, and metabolite analyses of facial and scalp skin surface lipids; lifestyle surveys and questionnaires; physical, motor, cognitive, and vascular function analyses; alopecia analysis; and comprehensive analyses of body odor components. Statistical analyses will be performed in 2 modes: one to train a joint probability distribution by combining a commercially available health care data set containing large amounts of relatively low-dimensional data with the cross-sectional data set described in this paper and another to individually investigate the relationships among the variables obtained in this study. Results Recruitment for this study started in October 2021 and ended in February 2022, with a total of 997 participants enrolled. The collected data will be used to build a joint probability distribution called a Virtual Human Generative Model. Both the model and the collected data are expected to provide information on the relationships between various health statuses. Conclusions As different degrees of health status correlations are expected to differentially affect individual health status, this study will contribute to the development of empirically justified interventions based on the population. International Registered Report Identifier (IRRID) DERR1-10.2196/47024